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Organ transplantation is often the last resort for treating end-stageillness, but the probability of a successful transplantation depends greatly oncompatibility between donors and recipients. Current medical practice relies oncoarse rules for donor-recipient matching, but is short of domain knowledgeregarding the complex factors underlying organ compatibility. In this paper, weformulate the problem of learning data-driven rules for organ matching usingobservational data for organ allocations and transplant outcomes. This problemdeparts from the standard supervised learning setup in that it involvesmatching the two feature spaces (i.e., donors and recipients), and requiresestimating transplant outcomes under counterfactual matches not observed in thedata. To address these problems, we propose a model based on representationlearning to predict donor-recipient compatibility; our model learnsrepresentations that cluster donor features, and applies donor-invarianttransformations to recipient features to predict outcomes for a givendonor-recipient feature instance. Experiments on semi-synthetic and real-worlddatasets show that our model outperforms state-of-art allocation methods andpolicies executed by human experts.

Learning Matching Representations for Individualized Organ Transplantation Allocation